Terraform becomes messy at scale—too much duplication, manual setup, and no orchestration. Terragrunt fixes this by automating state management, reducing repetition, and handling dependencies. In 2025, its new Stacks feature enables reusable infrastructure patterns, making it the better choice for multi-environment setups despite a small learning curve.

Alex Salazar, CEO of Arcade, argues that chatbots are useless without the power to take action. He claims real value in AI lies in agents that can actually do things—trigger workflows, manage authorizations, and connect to tools like Gmail or Slack. Salazar calls out why most “agents” never make it to production—security nightmares, high costs, latency, and poor accuracy—and says Arcade fixes this by giving developers the tools to build real, authorized agents fast. He challenges the industry’s obsession with data, insisting that AI’s future is about software, not datasets, and that the heavy lifting by OpenAI and Anthropic has already changed the rules.

Your AI agent isn’t failing because it’s dumb—it’s failing because you refuse to test it. Chiara Caratelli cuts through the hype to show why evaluations—not bigger models or fancier prompts—decide whether agents succeed in the real world. If you’re not stress-testing, simulating, and iterating on failures, you’re not building AI—you’re shipping experiments disguised as products.

Inspired by the French show “C’est pas sorcier,” “It’s Not Artificial” uses Google’s Gemini multi-speaker text-to-speech to recreate its conversational learning style. The project automatically generates full audio episodes — complete with distinct voices, languages, and topics — from simple inputs. What began as a nostalgic experiment now hints at a future where AI-driven conversations make education and training more personal, engaging, and human-like.

Graphs are everywhere, but often misunderstood. This blog busts common myths about knowledge graphs, explains why they’re faster and more flexible than you think, and shows how AI can help build them.

Using NotebookLM’s Video Overview, I turned my MLOps Coding Course from text into a full video series in just two days. What once felt like a month-long grind became a fast and creative process — demonstrating how AI can amplify expertise instead of replacing it.

Russ d'Sa shares how LiveKit went from a small open-source project during the pandemic to powering voice interfaces for giants like OpenAI. He talks about the turning point when LiveKit teamed up on ChatGPT’s voice features, the challenges of making AI sound human, and why voice could be the future of multimodal AI. It’s a story of chance, big shifts, and building the backbone of tomorrow’s AI.

LLM performance isn’t just about the model—it’s about the scaffolding we build around it. “Context Engineering” reframes the conversation: prompt design is the toy problem, while the real frontier is systematically engineering the information environments that shape model behavior. Surveying 1,400+ papers, this work defines the field’s taxonomy—retrieval, generation, processing, management—and shows how it powers RAG, memory, tool use, and multi-agent systems. The survey also reveals a paradox: LLMs can absorb increasingly complex contexts but remain clumsy at producing equally complex outputs. This tension signals a coming split between research obsessed with cramming more into context windows and the harder question of whether models can ever match the sophistication of what they’re given.